Generation of synthetic documents for data augmentation

A computer system, the computer system comprising:

• one or more processors, one or more computer-readable memories, one or more computer-readable storage medium, and program instructions stored on at least one of the one or more computer-readable storage medium for execution by at least one of the one or more processors via at least one of the one or more computer-readable memories, wherein the computer system is capable of performing a method comprising:
• identifying a first set of key value pairs (KVPs) within a set of business documents spanning multiple domains;
• creating a domain-agnostic model of spatial distribution of KVPs within the set;
• creating a domain-agnostic model of content distribution of KVPs within the set;
• grounding the domain-agnostic model of spatial distribution and the domain-agnostic model of content distribution using a second set of domain-specific KVPs to derive domain-specific models of spatial distribution and content distribution of KVPs within the second set;
• generating a set of synthetic domain-specific business documents using the derived domain-specific models of spatial distribution and content distribution;
• augmenting a training data set of a large language model (LLM) with the set of synthetic domain-specific business documents to create an augmented training data set;
• training the LLM using the augmented training data set;
• and
• performing, via the LLM, domain-specific document understanding tasks.

A computer program product, the computer program product comprising:

• one or more computer-readable storage medium and program instructions stored on at least one of the one or more computer-readable storage medium, the program instructions executable by a processor capable of performing a method, the method comprising:
• identifying a first set of key value pairs (KVPs) within a set of business documents spanning multiple domains;
• creating a domain-agnostic model of spatial distribution of KVPs within the set;
• creating a domain-agnostic model of content distribution of KVPs within the set;
• grounding the domain-agnostic model of spatial distribution and the domain-agnostic model of content distribution using a second set of domain-specific KVPs to derive domain-specific models of spatial distribution and content distribution of KVPs within the second set;
• generating a set of synthetic domain-specific business documents using the derived domain-specific models of spatial distribution and content distribution;
• augmenting a training data set of a large language model (LLM) with the set of synthetic domain-specific business documents to create an augmented training data set;
• training the LLM using the augmented training data set;
• and
• performing, via the LLM, domain-specific document understanding tasks.